Runtime performance has been a limiting factor for differentiable rendering systems in the past [1]}.
Most software rasterization techniques exceed the 100 barrier even for small scenes and render resolutions [2]}.
This limits their usefulness in real-world applications such as 3D reconstruction from high-resolution photographs.
Currently, the two most performant differentiable rendering methods that are able to process point-cloud data are Synsin [3]} and Pulsar [2]}.
Synsin, which is the default point render engine of PyTorch3D [5]}, splats each point to a disk and blends the \(K\)  nearest points of each pixel into the output image.
Pulsar converts each point to sphere and blends them with a similar approach as Soft Rasterizer [6]}.
Both methods are fully differentiable, meaning that the point position and color can be optimized during rendering.
